Structural design optimization subjected to uncertainty using fat Bezier curve

The success of genetic algorithms in structural design optimization depends largely on the geometric representation used. In this work, a geometric representation scheme using fat Bezier curve is proposed and evaluated to be efficient and effective in producing good results via a structure design problem subjected to uncertainty. This scheme facilitates the transmission of topological and shape characteristics across generations in the evolutionary process and amplifies the representation variability. A hybrid genetic algorithm coupled with the scheme to tackle structure topology optimization under uncertainty is also presented. The proposed hybrid algorithm integrates a simple local search strategy as the worst-case-scenario technique of anti-optimization with a constrained multi-objective genetic algorithm. Numerical results are also presented.